Systems | Development | Analytics | API | Testing

Building a sales dashboard, but don’t know which KPIs to include? 60+ sales pros share which metrics they track and their favorite sales dashboard types.

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Here are five reasons why you should use Integrate.io for Ecommerce data warehouse integration: Typically, Ecommerce data warehouse integration involves several complex steps that even the most advanced data engineer would struggle to execute. You need to build big data pipelines, cleanse data, make sure that data complies with data governance frameworks, and ensure data doesn't get degraded during the process. It's a challenge!

Never before has data become so prevalent in everything we do. Sorting out the best way to make sense of incoming terabytes of data has turned into an extreme sport. Likewise, it has become a life-or-death decision in every organization, regardless of their level of maturity, to determine an analytics strategy to harness the potential power of all that data without running the risk of overwhelming teams and paralyzing processes.

COVID-19 introduced an unprecedented level of volatility in world markets, and the shockwaves that arrived in its wake exposed a wide chasm between two main types of multinational organizations: Those with agile internal processes and those without. In a world built on complex and globalized supply chains, COVID-19 tested that internal agility, sometimes to breaking point.

In the second blog of the Universal Data Distribution blog series, we explored how Cloudera DataFlow for the Public Cloud (CDF-PC) can help you implement use cases like data lakehouse and data warehouse ingest, cybersecurity, and log optimization, as well as IoT and streaming data collection. A key requirement for these use cases is the ability to not only actively pull data from source systems but to receive data that is being pushed from various sources to the central distribution service.

Data integration plays a foundational role in the progression to data and analytics mastery.

Logs are one of the three key “pillars” of observability, and cloud environments are no exception. You can’t know what’s happening in your cloud without analyzing cloud service logs, which allow you to audit and monitor workflows within your cloud. That said, cloud logging is a unique beast in certain respects.

Our new feature helps you implement Type 2 slowly changing dimensions for your historical database analytics with no coding needed.